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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 莊永裕(Yung-Yu Chuang) | |
dc.contributor.author | Tz-Huan Huang | en |
dc.contributor.author | 黃子桓 | zh_TW |
dc.date.accessioned | 2021-06-13T00:28:17Z | - |
dc.date.available | 2007-07-30 | |
dc.date.copyright | 2007-07-30 | |
dc.date.issued | 2007 | |
dc.date.submitted | 2007-07-26 | |
dc.identifier.citation | [1] L. Q. Chen, X. Xie, X. Fan, W. Y. Ma, H. J. Zhang, and H. Q. Zhou. A visual attention model for adapting images on small displays. In ACM Multimedia System Journal, volume 9, 2003.
[2] M. Clauss, P. Bayerl, and H. Neumann. A statistical measure for evaluating regionsof-interest based attention algorithms. In Proceedings of 26th Pattern Recognition Symposium (DAGM 2004), pages 383–390, 2004. [3] B. J. Frey and D. Dueck. Clustering by passing messages between data points. Science, 315:972–976, 2007. [4] J. P. Gottlieb, M. Kusunoki, and M. E. Goldberg. The representation of visual salience in monkey parietal cortex. nat, 391:481–484, Jan. 1998. [5] G. Griffin, A. Holub, and P. Perona. Caltech-256 object category dataset. Technical Report UCB/CSD-04-1366, California Institute of Technology, 2007. [6] Y. Hu, D. Rajan, and L.-T. Chia. Robust subspace analysis for detecting visual attention regions in images. In Proceedings of ACM Multimedia 2005, pages 716–724, 2005. [7] L. Itti, C. Koch, and E. Niebur. A model of aliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11):1254–1259, 1998. [8] C. Koch and S. Ullman. Shifts in selective visual attention: towards the underlying neural circuitry. Hum Neurobiol, 4(4):219–227, 1985. [9] Y.-F. Ma and H.-J. Zhang. Contrast-based image attention analysis by using fuzzy growing. In Proceedings of ACM Multimedia 2003, pages 374–381, 2003. [10] A. Nguyen, V. Chandran, and S. Sridharan. Gaze tracking for region of interest coding in JPEG 2000. Signal Processing: Image Communication, 21(5):359–377, 2006. [11] NIST. http://www-nlpir.nist.gov/projects/trecvid/. [12] W. Osberger and A. J. Maeder. Automatic identification of perceptually important regions in an image. In Proceedings of ICPR 1998, pages 701–704, 1998. [13] R. Parasuraman. The Attentive Brain. The MIT Press, May 1998. [14] W. H. Press, B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling. Numerical Recipes in C : The Art of Scientific Computing. Cambridge University Press, 1992. [15] C. M. Privitera and L. W. Stark. Algorithms for defining visual regions-of-interest: Comparison with eye fixations. IEEE Transactions on Pattern Analysis Machine Intelligence, 22(9):970–982, 2000. [16] L. A. Rowe and R. Jain. Acm sigmm retreat report on future directions in multimedia research. ACM Transactions on Multimedia Computing, Communications, and Applications, 1(1):3–13, 2005. [17] H. A. Rowley, S. Baluja, and T. Kanade. http://vasc.ri.cmu.edu/NNFaceDetector/. [18] B. C. Russell, A. Torralba, K. P. Murphy, and W. T. Freeman. Labelme: a database and web-based tool for image annotation. MIT AI Lab Memo AIM-2005-025, 2005. [19] P. K. Sahoo, D. W. Slaaf, and T. A. Albert. Threshold selection using a minimal histogram entropy difference. Optical Engineering, 36:1976–1981, July 1997. [20] A. Santella and D. DeCarlo. Robust clustering of eye movement recordings for quantification of visual interest. In Proceedings of the 2004 symposium on Eye tracking research & applications, pages 27–34, 2004. [21] D. Scharstein and R. Szeliski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision, 47(3):7–42, 2002. [22] S. M. Seitz, B. Curless, J. Diebel, D. Scharstein, and R. Szeliski. A comparison and evaluation of multi-view stereo reconstruction algorithms. In Proceedings of CVPR 2006, pages 519–526, 2006. [23] C. G. Snoek, M. Worring, J. C. van Gemert, J.-M. Geusebroek, and A. W. Smeulders. The challenge problem for automated detection of 101 semantic concepts in multimedia. In ACM Multimedia, pages 421–430, 2006. [24] M. J. Swain and D. H. Ballard. Color indexing. International Journal of Computer Vision, 7(1):11–32, 1991. [25] A. M. Treisman and G. Gelade. A feature-integration theory of attention. Cognit Psychol, 12(1):97–136, January 1980. [26] L. von Ahn. Games with a purpose. IEEE Computer, pages 96–98, 2006. [27] L. von Ahn and L. Dabbish. Labeling images with a computer game. In Proceedings of ACM SIGCHI 2004, pages 319–326, 2004. [28] L. von Ahn, R. Liu, and M. Blum. Peekaboom: a game for locating objects in images. In Proceedings of ACM SIGCHI 2006, pages 55–64, 2006. [29] D. S.Wooding. Fixation maps: quantifying eye-movement traces. In Proceedings of the 2002 symposium on Eye tracking research & applications, pages 31–36, 2002. [30] X. Xie, H. Liu, S. Goumaz, andW.-Y. Ma. Learning user interest for image browsing on small-form-factor devices. In Proceedings of ACM SIGCHI 2005, pages 671–680, 2005. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/28894 | - |
dc.description.abstract | 本論文提出了一影像中使用者感興趣區域 (region of interest) 偵測之資料集 (benchmark)。使用者感興趣區域偵測在許多應用中極為有用,過去雖然有許多使用者感興趣區域之自動偵測演算法被提出,然而由於缺乏公開資料集,這些方法往往只測試了各自的小量資料而難以互相比較。從其它領域可以發現,基於公開資料集的可重製實驗與該領域突飛猛進密切相關,因此本論文填補了此領域之不足,我們提出名為「Photoshoot」的遊戲來蒐集人們對於感興趣區域的標記,並以這些標記來建立資料集。透過這個遊戲,我們已蒐集大量使用者對於感興趣區域的標記,並結合這些資料成為使用者感興趣區域模型。我們利用這些模型來量化評估五個使用者感興趣區域偵測演算法,此資料集也可更進一步作為基於學習理論演算法的測試資料,因此使基於學習理論的偵測演算法成為可能。 | zh_TW |
dc.description.abstract | This thesis presents a benchmark for region of interest (ROI) detection. ROI detection has many useful applications and many algorithms have been proposed to automatically detect ROIs. Unfortunately, due to the lack of benchmarks, these methods were often tested on small data sets that are not available to others, making fair comparisons of these methods difficult. Examples from many fields have shown that repeatable experiments using published benchmarks are crucial to the fast advancement of the fields. To fill the gap, this thesis presents our design for a collaborative game, called Photoshoot, to collect human ROI annotations for constructing an ROI benchmark. With this game, we have gathered a large number of annotations and fused them into aggregated ROI models. We use these models to evaluate five ROI detection algorithms quantitatively. Furthermore, by using the benchmark as training data, learning-based ROI detection algorithms become viable. | en |
dc.description.provenance | Made available in DSpace on 2021-06-13T00:28:17Z (GMT). No. of bitstreams: 1 ntu-96-R94922044-1.pdf: 9755432 bytes, checksum: 9ef12dcd62f9f6e6be24804ac62f2cab (MD5) Previous issue date: 2007 | en |
dc.description.tableofcontents | 口試委員會審定書 i
中文摘要 iii Abstract v 1 Introduction 1 2 Related work 5 3 Photoshoot 9 3.1 Game design 9 3.2 Implementation and other details 11 3.3 Statistics 12 4 ROI modeling 13 4.1 Target ROI model 14 4.2 Shoot ROI model 16 4.3 Results 18 5 Applications of Benchmarks 21 5.1 Quantitative evaluation 21 5.2 Learning from benchmarks 24 6 Conclusion and future work 25 Bibliography 27 | |
dc.language.iso | en | |
dc.title | 影像中使用者感興趣區域偵測之資料集 | zh_TW |
dc.title | A Benchmark for Region-of-Interest Detection in Images | en |
dc.type | Thesis | |
dc.date.schoolyear | 95-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 陳炳宇(Robin Bing-Yu Chen),徐宏民(Winston H. Hsu) | |
dc.subject.keyword | 使用者感興趣區域,資料集,遊戲, | zh_TW |
dc.subject.keyword | region of interest,benchmark,game, | en |
dc.relation.page | 29 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2007-07-26 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
顯示於系所單位: | 資訊工程學系 |
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